scholarly journals Houston’s rapid ozone increases: preconditions and geographic origins

2013 ◽  
Vol 10 (3) ◽  
pp. 260 ◽  
Author(s):  
Evan Couzo ◽  
Harvey E. Jeffries ◽  
William Vizuete
Keyword(s):  
Quality Standard ◽  
Health Concern ◽  
Ozone Pollution ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind ◽  
Geographic Origins ◽  
Early Afternoon ◽  
Modelling Studies ◽  
Low Wind Speeds

Environmental context Ozone pollution in Houston, Texas, has been a public health concern for decades. Unusually large hourly changes in observed ozone concentrations have been correlated with a greater likelihood of violating the federal air quality standard. We investigate the geographic and chemical origins of these large hourly increases, which should help regulators better control ozone violations. Abstract Many of Houston’s highest 8-h ozone (O3) peaks are characterised by increases in concentrations of at least 40ppb in 1h, or 60ppb in 2h. These rapid increases are called non-typical O3 changes (NTOCs). In 2004, the Texas Commission on Environmental Quality (TCEQ) developed a novel emissions control strategy aimed at eliminating NTOCs. The strategy limited routine and short-term emissions of ethene, propene, 1,3-butadiene and butene isomers, collectively called highly reactive volatile organic compounds (HRVOCs), which are released from petrochemical facilities. HRVOCs have been associated with NTOCs through field campaigns and modelling studies. This study analysed wind measurements and O3, formaldehyde (HCHO) and sulfur dioxide (SO2) concentrations from 2000 to 2011 at 25 ground monitors in Houston. NTOCs almost always occurred when monitors were downwind of petrochemical facilities. Rapid O3 increases were associated with low wind speeds; 75% of NTOCs occurred when the 3-h average wind speed preceding the event was less than 6.5kmh–1. Statistically significant differences in HCHO concentrations were seen between days with and without NTOCs. Early afternoon HCHO concentrations were greater on NTOC days. In the morning before an observed NTOC event, however, there were no significant differences in HCHO concentrations between days with and without NTOCs. Hourly SO2 concentrations also increased rapidly, exhibiting behaviour similar to NTOCs. Oftentimes, the SO2 increases preceded a NTOC. These findings show that, despite the apparent success of targeted HRVOC emission controls, further restrictions may be needed to eliminate the remaining O3 events.

scholarly journals Rapid coral mortality following unusually calm and hot conditions on Iriomote, Japan

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1728 ◽  
Author(s):  
Andrew H Baird ◽  
Sally A. Keith ◽  
Erika Woolsey ◽  
Ryuta Yoshida ◽  
Tohru Naruse
Keyword(s):  
Water Movement ◽  
Sea Surface ◽  
Bleaching Event ◽  
Coral Mortality ◽  
Average Wind Speed ◽  
Common Cause ◽  
Wind Speeds ◽  
Average Wind ◽  
The Mean ◽  
Low Wind Speeds

Coral bleaching can be induced by many different stressors, however, the most common cause of mass bleaching in the field is higher than average sea surface temperatures (SST). Here, we describe an unusual bleaching event that followed very calm sea conditions combined with higher than average SST. Patterns of mortality differed from typical bleaching in four ways: 1) mortality was very rapid; 2) a different suite of species were most affected; 3) tissue mortality in Acropora spp. was often restricted to the center of the colony; 4) the event occurred early in summer. The two weeks prior to the event included 8 days where the average wind speed was less than 3 ms-1. In addition, SSTs in the weeks preceding and during the event were 1.0-1.5°C higher than the mean for the last 30 years. We hypothesize that this unusual bleaching event was caused by anoxia resulting from a lack of water movement induced by low wind speeds combined with high SST.

scholarly journals Rapid coral mortality following doldrums-like conditions on Iriomote, Japan

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1728 ◽  
Author(s):  
Andrew H Baird ◽  
Sally A. Keith ◽  
Erika Woolsey ◽  
Ryuta Yoshida ◽  
Tohru Naruse
Keyword(s):  
Water Movement ◽  
Sea Surface ◽  
Bleaching Event ◽  
Coral Mortality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind ◽  
Thermal Bleaching ◽  
The Mean ◽  
Low Wind Speeds

Coral bleaching can be induced by many different stressors, however, the most common cause of mass bleaching in the field is high sea temperatures (SST). Here, we describe an unusual bleaching event that followed very calm sea conditions combined with higher than average sea surface temperatures (SST). Patterns of mortality differed from typical thermal bleaching in four ways: 1) mortality was very rapid; 2) the suite of species most affected was different; 3) tissue mortality in Acropora spp. was often restricted to the center of the colony; 4) the event occurred early in the summer. The two weeks prior to the event included 8 days where the average wind speed was less than 3 ms-1. In addition, SSTs in the weeks preceding this event were 1.0-1.5°C higher than the mean for the last 30 years. We hypothesize that the lack of water movement induced by low wind speeds combined with high SST to cause colonies anoxic stress resulting in this unusual bleaching event.

SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

2018 ◽  
pp. 28-50
Author(s):  
S. G. Ignatiev ◽  
S. V. Kiseleva
Keyword(s):  
Energy Storage ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Demand ◽  
Diesel Generator ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

scholarly journals Analysis of Performance of the Wind-Driven Pulverizing Aerator Based on Average Wind Speeds in the Conditions of Góreckie Lake

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2796
Author(s):  
Andrzej Osuch ◽  
Ewa Osuch ◽  
Stanisław Podsiadłowski ◽  
Piotr Rybacki
Keyword(s):  
Mathematical Model ◽  
Wind Speed ◽  
Water Pump ◽  
Average Wind Speed ◽  
Flow Rates ◽  
Wind Speeds ◽  
Average Wind ◽  
Flow Through ◽  
Analysis Of Performance ◽  
Research Period

In the introduction to this paper, the characteristics of Góreckie lake and the construction and operation of the wind-driven pulverizing aerator are presented. The purpose of this manuscript is to determine the efficiency of the pulverizing aerator unit in the windy conditions of Góreckie Lake. The efficiency of the pulverization aerator depends on the wind conditions at the lake. It was necessary to conduct thorough research to determine the efficiency of water flow through the pulverization segment (water pump). It was necessary to determine the rotational speed of the paddle wheel, which depended on the average wind speed. Throughout the research period, measurements of hourly average wind speed were carried out. It was possible to determine the efficiency of the machine by developing a dedicated mathematical model. The latest method was used in the research, consisting of determining the theoretical volumetric flow rates of water in the pulverizing aerator unit, based on average hourly wind speeds. Pulverization efficiency under the conditions of Góreckie Lake was determined based on 6600 average wind speeds for spring, summer and autumn, 2018. Based on the model, the theoretical efficiency of the machine was calculated, which, under the conditions of Góreckie Lake, amounted to 75,000 m3 per year.

scholarly journals Formation and distribution of sea-surface microlayers

2010 ◽  
Vol 7 (4) ◽  
pp. 5719-5755 ◽  
Author(s):  
O. Wurl ◽  
E. Wurl ◽  
L. Miller ◽  
K. Johnson ◽  
S. Vagle
Keyword(s):  
Wind Speed ◽  
Primary Production ◽  
Sea Surface ◽  
Surface Microlayer ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind ◽  
The Pacific ◽  
Sea Surface Microlayer ◽  
Natural Surfactants

Abstract. Results from a study of surfactants in the sea-surface microlayer (SML) in different regions of the ocean (subtropical, temperate, polar) suggest that this interfacial layer between the ocean and atmosphere covers the ocean's surface to a significant extent. Threshold values at which primary production acts as a significant source of natural surfactants have been derived from the enrichment of surfactants in the SML relative to underlying water and local primary production. Similarly, we have also derived a wind speed threshold at which the SML is disrupted. The results suggest that surfactant enrichment in the SML is typically greater in oligotrophic regions of the ocean than in more productive waters. Furthermore, the enrichment of surfactants persisted at wind speeds of up to 10 m s−1 without any observed depletion above 5 m s−1. This suggests that the SML is stable enough to exist even at the global average wind speed of 6.6 m s−1. Global maps of primary production and wind speed are used to estimate the ocean's SML coverage. The maps indicate that wide regions of the Pacific and Atlantic Oceans between 30° N and 30° S are more significantly affected by the SML than northern of 30° N and southern of 30° S due to higher productivity (spring/summer blooms) and wind speeds exceeding 12 m s−1 respectively.

scholarly journals The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

2019 ◽  
Vol 4 (2) ◽  
pp. 343-353 ◽  
Author(s):  
Tyler C. McCandless ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Power Conversion ◽  
Jensen’S Inequality ◽  
Jensen's Inequality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

scholarly journals Pillars of Solution for the Problem of Winter PM2.5 Variability in Fresno—Effects of Local Meteorology and Emissions

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 312
Author(s):  
Thishan Karandana Gamalathge ◽  
Mark Green ◽  
William Stockwell
Keyword(s):  
Atmospheric Stability ◽  
Winter Season ◽  
Mass Composition ◽  
Yearly Variation ◽  
Average Wind Speed ◽  
Precipitation Frequency ◽  
Wind Speeds ◽  
Average Wind ◽  
Long Term Trends ◽  
The Relationship

The mass composition of Particulate Matter (PM) with an aerodynamic diameter of 2.5 microns (PM2.5) in San Joaquin Valley (SJV) is dominated by ammonium nitrate (NH4NO3), a secondary pollutant. The goal of this research was the investigation of the relationship between emissions, meteorology and PM2.5 concentrations in Fresno for the winter season. It was found that location of sites near emission sources such as freeways compared with residential sites strongly affected measured PM2.5 concentrations. It was found that although long-term trends showed declines in both emissions and PM2.5 concentrations, there was substantial variability between the years in the PM2.5–emissions relationship. Much of the yearly variation in the relationship between emissions and PM2.5 concentrations can be attributed to yearly variations in weather, such as atmospheric stability, precipitation frequency and average wind speed. There are moderate correlations between PM2.5 concentrations and temperature differences between nearby surface stations at varying elevations which explains some of the daily and seasonal variation in PM2.5. Occurrence of precipitation was related to low PM 2.5, although the higher wind speeds and lower atmospheric stability associated with precipitation likely explain some of the low PM2.5 as well as washout of PM.

scholarly journals Field Study on the Microclimate of Public Spaces in Traditional Residential Areas in a Severe Cold Region of China

2019 ◽  
Vol 16 (16) ◽  
pp. 2986 ◽  
Author(s):  
Yujie Lin ◽  
Yumeng Jin ◽  
Hong Jin
Keyword(s):  
Wind Speed ◽  
Temperature Difference ◽  
Public Spaces ◽  
Average Wind Speed ◽  
Coverage Ratio ◽  
Wind Speeds ◽  
Average Temperature ◽  
Average Temperature Difference ◽  
Average Wind ◽  
The Difference

As residential environment science advances, the environmental quality of outdoor microclimates has aroused increasing attention of scholars majoring in urban climate and built environments. Taking the microclimate of a traditional residential area in a severe cold city as the study object, this study explored the influence of spatial geometry factors on the microclimate of streets and courtyards by field measurements, then compared the differences in microclimate of distinct public spaces. The results are as follows. (1) The temperature of a NE-SW (Northeast-Southwest) oriented street was higher than that of a NW-SE (Northwest-Southeast) oriented street in both summer and winter, with an average temperature difference of 0.7–1.4 °C. The wind speeds in the latter street were slower, and the difference in average wind speed was 0.2 m/s. (2) In the street with a higher green coverage ratio, the temperature was much lower, a difference that was more obvious in summer. The difference in mean temperature was up to 1.2 °C. The difference in wind speed between the two streets was not obvious in winter, whereas the wind speed in summer was significantly lower for the street with a higher green coverage ratio, and the difference in average wind speed was 0.7 m/s. (3) The courtyards with higher SVF (sky view factor) had higher wind speeds in winter and summer, and the courtyards with larger SVF values had higher temperatures in summer, with an average temperature difference of 0.4 °C. (4) When the spaces had the same SVF values and green coverage ratios, the temperature of the street and courtyard were very similar, in both winter and summer. The wind speed of the street was significantly higher than the courtyard in summer, and the wind speed difference was 0.4 m/s.

scholarly journals Monitoring Sand Drift Potential and Sand Dune Mobility over the Last Three Decades (Khartouran Erg, Sabzevar, NE Iran)

2021 ◽  
Vol 13 (16) ◽  
pp. 9050
Author(s):  
Mohammad Reza Rahdari ◽  
Andrés Rodríguez-Seijo
Keyword(s):  
Wind Speed ◽  
Sand Dune ◽  
Sand Dunes ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind ◽  
Sand Drift ◽  
Drift Potential ◽  
Long Time ◽  
Ne Iran

Aeolian sediments cover about 6% of the earth’s surface, of which 97% occur in arid regions, and these sediments cover about 20% of the world’s lands. Sand drifts can harm sensitive ecosystems; therefore, this research has aimed to study wind regimes and the monitoring of sand drift potential and dune mobility in the Khartouran Erg (NE Iran). The study investigated 30 years of wind speed and direction to better understand sand dune mobility processes using the Fryberger and Tsoar methods. The results of the wind regime study showed that the eastern (33.4%) and northeastern (14.3%) directions were more frequent, but the study of winds greater than the threshold (6 m/s) in winter, spring, and autumn indicated the dominance of eastern and northern wind directions. Findings of calm winds showed that winters (40.4%) had the highest frequency, and summers (15%) had the lowest frequency; the annual frequency was 30%. The average wind speed in summers was the highest (4.38 m/s), and, in the winters, it was the lowest (2.28 m/s); the annual average wind speed was 3.3 m/s. The annual drift potential (DP = 173 VU) showed that it was categorized as low class, and the winds carried sand to the southwest. The monitoring of drift potential showed that there was a sharp increase between 2003 and 2008, which could have been attributed to a change in wind speeds in the region. Unite directional index, the index of directional variability, has been alternating from 0.3 to 0.6 for 30 years. Furthermore, monitoring of sand mobility recorded a value from 0.1 to 0.4, and the lowest and highest values were registered from 0.08 to 0.9, with an average of 0.27. Finally, it can be concluded that sand dunes have been fixed for a long time, and the intensity of the mobility index is affected by climate changes.

scholarly journals The Super-Turbine Wind Power Conversion Paradox: Using Machine Learning to Reduce Errors Caused by Jensen’s Inequality

2019 ◽  
Author(s):  
Tyler C. McCandless ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Power Conversion ◽  
Jensen’S Inequality ◽  
Jensen's Inequality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared to a super-turbine power conversion for a hypothetical wind farm of 100 2-MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with 2 m s−1 standard deviation of wind speeds as a consequence of Jensen’s Inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen’s Inequality. These significant differences can lead to wind power forecasters over-estimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicates that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen’s Inequality.

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